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Annealed fractional Lévy–Itō diffusion models for protein generation

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.1016/j.csbj.2024.04.009
AuthorSearch for: 1ORCID identifier: https://orcid.org/0000-0001-6515-2556; Search for: ; Search for: ; Search for: ORCID identifier: https://orcid.org/0000-0002-9198-6439
Affiliation
  1. National Research Council of Canada. Digital Technologies
FunderSearch for: National Research Council Canada
FormatText, Article
Subjectdiffusion; fractional; generative model; Lévy–Itō; noising; protein; stable distribution; stochastic differential equation; score
Abstract
Protein generation has numerous applications in designing therapeutic antibodies and creating new drugs. Still, it is a demanding task due to the inherent complexities of protein structures and the limitations of current generative models. Proteins possess intricate geometry, and sampling their conformational space is challenging due to its high dimensionality. This paper introduces novel Markovian and non-Markovian generative diffusion models based on fractional stochastic differential equations and the Lévy distribution, allowing for a more effective exploration of the conformational space. The approach is applied to a dataset of 40,000 proteins and evaluated in terms of Fréchet distance, fidelity, and diversity, outperforming the state-of-the-art by 25.4%, 35.8%, and 11.8%, respectively.
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PublisherElsevier
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  • Computational and Structural Biotechnology Journal 23, no. C (17 April 2024): 1641–1653.
LanguageEnglish
Peer reviewedYes
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Record identifierc1cf08b1-a648-4900-b6b5-06cec6025624
Record created2024-04-22
Record modified2024-04-22
Date modified: